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Algebraic Geometry and Statistical Learning Theory
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Details

  • 13 b/w illus.
  • Page extent: 300 pages
  • Size: 228 x 152 mm
  • Weight: 0.56 kg
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Hardback

 (ISBN-13: 9780521864671)

  • Also available in Adobe eBook
  • Published September 2009

Temporarily unavailable - available from May 2017

$93.00 (P)

Sure to be influential, Watanabe’s book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are singular: mixture models, neural networks, HMMs, Bayesian networks, stochastic context-free grammars are major examples. The theory achieved here underpins accurate estimation techniques in the presence of singularities.

Contents

Preface; 1. Introduction; 2. Singularity theory; 3. Algebraic geometry; 4. Zeta functions and singular integral; 5. Empirical processes; 6. Singular learning theory; 7. Singular learning machines; 8. Singular information science; Bibliography; Index.

Review

"Overall, the many insightful remarks and simple direct language make the book a pleasure to read."
Shaowei Lin, Mathematical Reviews

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